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      Ethnic differences in SARS-CoV-2 infection and COVID-19-related hospitalisation, intensive care unit admission, and death in 17 million adults in England: an observational cohort study using the OpenSAFELY platform

      research-article
      , PhD a , * , * , , PhD a , * , , MRCGP e , * , , PhD e , , PhD a , , MPharm e , , PhD c , , Prof, PhD a , , PhD a , , Prof, PhD b , , PhD a , , PhD e , , PhD c , f , , PhD g , , BA e , , PhD e , , MPhil e , , MPhil e , , MB e , , DPhil e , , MPH e , , MSc e , , MRCS e , , BSc g , , MRCGP g , , BSc g , , MSci g , , Prof, PhD a , , PhD a , , Prof, MSc b , , Prof, PhD d , , PhD h , , PhD h , , Prof, FMedSci i , , Prof, MD j , , Prof, FMedSci a , , , MRCPsych e ,
      Lancet (London, England)
      The Author(s). Published by Elsevier Ltd.
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          Abstract

          Background

          COVID-19 has disproportionately affected minority ethnic populations in the UK. Our aim was to quantify ethnic differences in SARS-CoV-2 infection and COVID-19 outcomes during the first and second waves of the COVID-19 pandemic in England.

          Methods

          We conducted an observational cohort study of adults (aged ≥18 years) registered with primary care practices in England for whom electronic health records were available through the OpenSAFELY platform, and who had at least 1 year of continuous registration at the start of each study period (Feb 1 to Aug 3, 2020 [wave 1], and Sept 1 to Dec 31, 2020 [wave 2]). Individual-level primary care data were linked to data from other sources on the outcomes of interest: SARS-CoV-2 testing and positive test results and COVID-19-related hospital admissions, intensive care unit (ICU) admissions, and death. The exposure was self-reported ethnicity as captured on the primary care record, grouped into five high-level census categories (White, South Asian, Black, other, and mixed) and 16 subcategories across these five categories, as well as an unknown ethnicity category. We used multivariable Cox regression to examine ethnic differences in the outcomes of interest. Models were adjusted for age, sex, deprivation, clinical factors and comorbidities, and household size, with stratification by geographical region.

          Findings

          Of 17 288 532 adults included in the study (excluding care home residents), 10 877 978 (62·9%) were White, 1 025 319 (5·9%) were South Asian, 340 912 (2·0%) were Black, 170 484 (1·0%) were of mixed ethnicity, 320 788 (1·9%) were of other ethnicity, and 4 553 051 (26·3%) were of unknown ethnicity. In wave 1, the likelihood of being tested for SARS-CoV-2 infection was slightly higher in the South Asian group (adjusted hazard ratio 1·08 [95% CI 1·07–1·09]), Black group (1·08 [1·06–1·09]), and mixed ethnicity group (1·04 [1·02–1·05]) and was decreased in the other ethnicity group (0·77 [0·76–0·78]) relative to the White group. The risk of testing positive for SARS-CoV-2 infection was higher in the South Asian group (1·99 [1·94–2·04]), Black group (1·69 [1·62–1·77]), mixed ethnicity group (1·49 [1·39–1·59]), and other ethnicity group (1·20 [1·14–1·28]). Compared with the White group, the four remaining high-level ethnic groups had an increased risk of COVID-19-related hospitalisation (South Asian group 1·48 [1·41–1·55], Black group 1·78 [1·67–1·90], mixed ethnicity group 1·63 [1·45–1·83], other ethnicity group 1·54 [1·41–1·69]), COVID-19-related ICU admission (2·18 [1·92–2·48], 3·12 [2·65–3·67], 2·96 [2·26–3·87], 3·18 [2·58–3·93]), and death (1·26 [1·15–1·37], 1·51 [1·31–1·71], 1·41 [1·11–1·81], 1·22 [1·00–1·48]). In wave 2, the risks of hospitalisation, ICU admission, and death relative to the White group were increased in the South Asian group but attenuated for the Black group compared with these risks in wave 1. Disaggregation into 16 ethnicity groups showed important heterogeneity within the five broader categories.

          Interpretation

          Some minority ethnic populations in England have excess risks of testing positive for SARS-CoV-2 and of adverse COVID-19 outcomes compared with the White population, even after accounting for differences in sociodemographic, clinical, and household characteristics. Causes are likely to be multifactorial, and delineating the exact mechanisms is crucial. Tackling ethnic inequalities will require action across many fronts, including reducing structural inequalities, addressing barriers to equitable care, and improving uptake of testing and vaccination.

          Funding

          Medical Research Council.

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          Most cited references36

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          Is Open Access

          OpenSAFELY: factors associated with COVID-19 death in 17 million patients

          COVID-19 has rapidly impacted on mortality worldwide. 1 There is unprecedented urgency to understand who is most at risk of severe outcomes, requiring new approaches for timely analysis of large datasets. Working on behalf of NHS England we created OpenSAFELY: a secure health analytics platform covering 40% of all patients in England, holding patient data within the existing data centre of a major primary care electronic health records vendor. Primary care records of 17,278,392 adults were pseudonymously linked to 10,926 COVID-19 related deaths. COVID-19 related death was associated with: being male (hazard ratio 1.59, 95%CI 1.53-1.65); older age and deprivation (both with a strong gradient); diabetes; severe asthma; and various other medical conditions. Compared to people with white ethnicity, black and South Asian people were at higher risk even after adjustment for other factors (HR 1.48, 1.29-1.69 and 1.45, 1.32-1.58 respectively). We have quantified a range of clinical risk factors for COVID-19 related death in the largest cohort study conducted by any country to date. OpenSAFELY is rapidly adding further patients’ records; we will update and extend results regularly.
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            Collider bias undermines our understanding of COVID-19 disease risk and severity

            Numerous observational studies have attempted to identify risk factors for infection with SARS-CoV-2 and COVID-19 disease outcomes. Studies have used datasets sampled from patients admitted to hospital, people tested for active infection, or people who volunteered to participate. Here, we highlight the challenge of interpreting observational evidence from such non-representative samples. Collider bias can induce associations between two or more variables which affect the likelihood of an individual being sampled, distorting associations between these variables in the sample. Analysing UK Biobank data, compared to the wider cohort the participants tested for COVID-19 were highly selected for a range of genetic, behavioural, cardiovascular, demographic, and anthropometric traits. We discuss the mechanisms inducing these problems, and approaches that could help mitigate them. While collider bias should be explored in existing studies, the optimal way to mitigate the problem is to use appropriate sampling strategies at the study design stage.
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              Modelling the impact of testing, contact tracing and household quarantine on second waves of COVID-19

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                Author and article information

                Journal
                Lancet
                Lancet
                Lancet (London, England)
                The Author(s). Published by Elsevier Ltd.
                0140-6736
                1474-547X
                30 April 2021
                30 April 2021
                Affiliations
                [a ]Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
                [b ]Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, UK
                [c ]Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
                [d ]Department of Health Services Research and Policy, London School of Hygiene & Tropical Medicine, London, UK
                [e ]The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
                [f ]National Institute for Health Research Health Protection Research Unit in Immunisation, London, UK
                [g ]TPP, Leeds, UK
                [h ]Intensive Care National Audit and Research Centre, London, UK
                [i ]Diabetes Research Centre, University of Leicester, Leicester, UK
                [j ]Medical Research Council Unit for Lifelong Health and Ageing, University College London, London, UK
                Author notes
                [* ]Correspondence to: Dr Rohini Mathur, Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
                [*]

                Contributed equally

                [†]

                Joint senior authors

                Article
                S0140-6736(21)00634-6
                10.1016/S0140-6736(21)00634-6
                8087292
                33939953
                7b1c13ea-6877-47c0-9218-7e8dfcb2dc0a
                © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

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